Stas,
Thank you for your help! I think the -gllamn- approach may not be the
best option for this data, as the health facilities were not randomly
selected for participation.
The -svyset- approach may work, but I have one concern. We know the
selection probabilities at post-test, but not at pre-test. This is
because at pre-test providers were not trained yet, and they were not
selected for training until after the pre-test data was collected. So,
basically, we don't know at pre-test who was going to be trained and who
was not going to be trained.
It may be safe to assume that this lack of knowledge prevented the
pre-test sample from over-representing providers who would be trained
(as was the case with the post-test sample where data collectors
purposely over-sampled trained providers in an effort to capture enough
cases for meaningful comparisons between trained and untrained
providers).
I do want to complete a pre to post-test analysis. Do you see any
problems with applying different sampling weights to pre and post data
using the svyset approach?
gv
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Stas
Kolenikov
Sent: Thursday, April 05, 2007 10:43 AM
To: [email protected]
Subject: Re: st: GEE and weighting
I guess if you are able to track the selection probabilities for
individuals really well, you could use
svyset health_center [pw=weight]
svy: logit whatever
Or, if you have selection probabilities for both health centers and
individuals within those, you could use -gllamm- for two-level modeling
with weights for both levels.
-xtgee- would give you some gain in precision over -logit- if the
correlation structure is specified correctly, but other than that,
-logit- should do fine, too. The -svyset- here is effectively analogous
to specifying -, cluster(cpihfano)- with the (quasi-)likelihood
commands, which is another way to correct for correlation/clustering in
your data.
On 4/4/07, Gwyneth Vance <[email protected]> wrote:
> What does one do with binary, clustered data that must be weighted?
>
> I am working on a project using Stata 9. The goal is to develop
> models of various binary outcome measures pertaining to improved
> counseling by health providers. I am, however, running into several
> challenges. The first is that the sample taken was a cluster sample.
> Individuals were interviewed at various health centers, so the health
> center was the primary sampling unit-health centers were selected for
> participation and then the individual study participants. Originally,
> I thought that I could use regression with GEE to account for the
> clustering in the data; however, I discovered a second problem that
> may limit my ability to do so. Within each cluster, the individuals
> sampled were not sampled in equal proportion on an important variable,
which was provider training.
> In other words, clients who received counseling from trained providers
> were over-represented in the sample.
>
> I thought the solution would be to apply a sample weight (pweight
> command in Stata), but Stata does not allow the pweight to vary by
> unit within a panel. That is, the individuals within a cluster are
> not allowed to have their own weight, only the panel or cluster may be
> weighted. Below are the commands I keyed in, and the error message
> that I received.
>
> Command:
> iis cpihfano
> xtgee cpi41 cpi2 ce39 [pweight = weight], family(binomial 1)
> link(logit)
> corr(exchangeable)
>
> Error Message:
> weight must be constant within cpihfano r(199);
>
> I have done a bit of research on the topic, but am getting no where
> other to discover that this problem may be; as yet, unsolved (please
> refer to this link for further explanation
> http://www.stata.com/support/faqs/stat/xtweight.html ).
>
> So, what can one do with binary, clustered data that should be
weighted?
> Does anyone know if progress has been made on this front? What
> solutions have others devised in similar situations? I can provide
> more detail if necessary.
>
> Gwyneth
>
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>
--
Stas Kolenikov
http://stas.kolenikov.name
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